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Pre-trained models are widely used in the tasks of natural languageprocessing nowadays. However, in the specific field of text simplification, theresearch on improving pre-trained models is still blank. In this work, wepropose a continued pre-training method for text simplification. Specifically,we propose a new masked language modeling (MLM) mechanism, which does notrandomly mask words but only masks simple words. The new mechanism can make themodel learn to generate simple words. We use a small-scale simple text datasetfor continued pre-training and employ two methods to identify simple words fromthe texts. We choose BERT, a representative pre-trained model, and continuepre-training it using our proposed method. Finally, we obtain SimpleBERT, whichsurpasses BERT in both lexical simplification and sentence simplification tasksand has achieved state-of-the-art results on multiple datasets. What's more,SimpleBERT can replace BERT in existing simplification models withoutmodification.

SimpleBERT: A Pre-trained Model That Learns to Generate Simple Words